Development of a Software for Treat-To-Target Strategy Implementation and Increasing Quality of Life in Patients with Inflammatory Bowel Disease.

The management of each form of the inflammatory bowel disease (IBD), ulcerative colitis (UC) and Crohn's disease (CD), represents a challenge for the clinician and patient. The treatment paradigm was shifted from achievement of a symptomatic control of the disease, to the prevention of bowel damage, disease progression and disability, and better quality of life. These goals were related with the treat-to-target (T2T) strategies developed for a proper treatment optimization.

Automated Gleason grading of prostate cancer using transfer learning from general-purpose deep-learning networks.

Two deep-learning algorithms designed to classify images according to the Gleason grading system that used transfer learning from two well-known general-purpose image classification networks (AlexNet and GoogleNet) were trained on Hematoxylin-Eosin histopathology stained microscopy images with prostate cancer. The dataset consisted of 439 images asymmetrically distributed in four Gleason grading groups. Mean and standard deviation accuracy for AlexNet derivate network was of 61.

MR spectroscopy of the liver - a reliable non-invasive alternative for evaluating non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is one of the most common conditions worldwide that targets the liver parenchyma. NAFLD represents an intrahepatic triglyceride accumulation in the absence of excessive alcohol consumption and other diseases that affect the liver parenchyma. The current "gold standard" for evaluating the amount of intrahepatic fat is represented by liver biopsy, but many patients are reluctant and hardly accept undergoing this procedure due to its invasive nature.

Microglial morphology determined with confocal and two-photon laser scanning microscopy.

Microglia are the first and main form of active immune defense in the nervous system. The immune status of microglia is directly correlated to their morphology. Therefore, microglia morphology is used to distinguish between active and surveilling microglia.